pibblefit.RdCreate pibblefit object
pibblefit(D, N, Q, coord_system, iter = NULL, alr_base = NULL, ilr_base = NULL, Eta = NULL, Lambda = NULL, Sigma = NULL, Sigma_default = NULL, Y = NULL, X = NULL, upsilon = NULL, Theta = NULL, Xi = NULL, Xi_default = NULL, Gamma = NULL, init = NULL, names_categories = NULL, names_samples = NULL, names_covariates = NULL)
| D | number of multinomial categories |
|---|---|
| N | number of samples |
| Q | number of covariates |
| coord_system | coordinate system objects are represented in (options include "alr", "clr", "ilr", and "proportions") |
| iter | number of posterior samples |
| alr_base | integer category used as reference (required if coord_system=="alr") |
| ilr_base | (D x D-1) contrast matrix (required if coord_system=="ilr") |
| Eta | Array of samples of Eta |
| Lambda | Array of samples of Lambda |
| Sigma | Array of samples of Sigma (null if coord_system=="proportions") |
| Sigma_default | Array of samples of Sigma in alr base D, used if coord_system=="proportions" |
| Y | DxN matrix of observed counts |
| X | QxN design matrix |
| upsilon | scalar prior dof of inverse wishart prior |
| Theta | prior mean of Lambda |
| Xi | Matrix of prior covariance for inverse wishart (null if coord_system=="proportions") |
| Xi_default | Matrix of prior covariance for inverse wishart in alr base D (used if coord_system=="proportions") |
| Gamma | QxQ covariance matrix prior for Lambda |
| init | matrix initial guess for Lambda used for optimization |
| names_categories | character vector |
| names_samples | character vector |
| names_covariates | character vector |
object of class pibblefit